Nonparametric Bayesian Methods: Models, Algorithms, and Applications III

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  • เผยแพร่เมื่อ 5 พ.ย. 2024

ความคิดเห็น • 10

  • @klio1095
    @klio1095 7 ปีที่แล้ว +6

    Very mathematical, but at the same time cleared the whole set of stochastic processes and the nonparametric toolbox for further use! Thanks!

  • @ireallyamrumi
    @ireallyamrumi 5 ปีที่แล้ว +4

    The first 10 minutes REALLY need to be the standard storytelling

  • @666Tomato666
    @666Tomato666 2 ปีที่แล้ว

    it feels to me like this is two levels of abstraction above what's necessary to implement those algorithms, which is again like two or three levels of abstraction above what's necessary to actually use them...

  • @olivierma2691
    @olivierma2691 5 ปีที่แล้ว

    Such a great talk!

  • @nicholasdewaal
    @nicholasdewaal 5 ปีที่แล้ว

    Is there some kind of general approach to finding a measure like what's done at 5:05 for other similar problems other than guessing?

  • @JakeYeung
    @JakeYeung 4 ปีที่แล้ว +4

    Bang, bang, and bang. Then throw that in there, and you got an integral that an undergrad can do.
    ...
    *nervous silence from the audience*

    • @un1que212
      @un1que212 3 ปีที่แล้ว

      The best line of all time. Imagine being a TA for the calculus II class in which the prof. put that on the exam. I can hear the trenchant complaints already.

  • @rewtnode
    @rewtnode 6 ปีที่แล้ว

    Wtf is a beta 1 com alpha distribution? Or what does he say at 8:00

    • @liamconnell6117
      @liamconnell6117 6 ปีที่แล้ว +4

      Beta(1,alpha)

    • @yunzheli8784
      @yunzheli8784 4 ปีที่แล้ว

      1/B(1, alpha) (1 - eta)^(alpha-1) is the probability density function of Beta(1, alpha).